The value of computed tomography in assessing the risk of death in COVID-19 patients presenting to the emergency room.
COVID-19
Clinical prediction rule
Multidetector computed tomography
Prognosis
Journal
European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774
Informations de publication
Date de publication:
Dec 2021
Dec 2021
Historique:
received:
27
09
2020
accepted:
09
04
2021
revised:
22
03
2021
pubmed:
13
5
2021
medline:
17
11
2021
entrez:
12
5
2021
Statut:
ppublish
Résumé
The aims of this study were to develop a multiparametric prognostic model for death in COVID-19 patients and to assess the incremental value of CT disease extension over clinical parameters. Consecutive patients who presented to all five of the emergency rooms of the Reggio Emilia province between February 27 and March 23, 2020, for suspected COVID-19, underwent chest CT, and had a positive swab within 10 days were included in this retrospective study. Age, sex, comorbidities, days from symptom onset, and laboratory data were retrieved from institutional information systems. CT disease extension was visually graded as < 20%, 20-39%, 40-59%, or ≥ 60%. The association between clinical and CT variables with death was estimated with univariable and multivariable Cox proportional hazards models; model performance was assessed using k-fold cross-validation for the area under the ROC curve (cvAUC). Of the 866 included patients (median age 59.8, women 39.2%), 93 (10.74%) died. Clinical variables significantly associated with death in multivariable model were age, male sex, HDL cholesterol, dementia, heart failure, vascular diseases, time from symptom onset, neutrophils, LDH, and oxygen saturation level. CT disease extension was also independently associated with death (HR = 7.56, 95% CI = 3.49; 16.38 for ≥ 60% extension). cvAUCs were 0.927 (bootstrap bias-corrected 95% CI = 0.899-0.947) for the clinical model and 0.936 (bootstrap bias-corrected 95% CI = 0.912-0.953) when adding CT extension. A prognostic model based on clinical variables is highly accurate in predicting death in COVID-19 patients. Adding CT disease extension to the model scarcely improves its accuracy. • Early identification of COVID-19 patients at higher risk of disease progression and death is crucial; the role of CT scan in defining prognosis is unclear. • A clinical model based on age, sex, comorbidities, days from symptom onset, and laboratory results was highly accurate in predicting death in COVID-19 patients presenting to the emergency room. • Disease extension assessed with CT was independently associated with death when added to the model but did not produce a valuable increase in accuracy.
Identifiants
pubmed: 33978822
doi: 10.1007/s00330-021-07993-9
pii: 10.1007/s00330-021-07993-9
pmc: PMC8113019
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
9164-9175Investigateurs
Massimo Costantini
(M)
Roberto Grilli
(R)
Massimiliano Marino
(M)
Giulio Formoso
(G)
Debora Formisano
(D)
Emanuela Bedeschi
(E)
Cinzia Perilli
(C)
Elisabetta La Rosa
(E)
Eufemia Bisaccia
(E)
Ivano Venturi
(I)
Massimo Vicentini
(M)
Cinzia Campari
(C)
Francesco Gioia
(F)
Serena Broccoli
(S)
Pamela Mancuso
(P)
Marco Foracchia
(M)
Mirco Pinotti
(M)
Nicola Facciolongo
(N)
Laura Trabucco
(L)
Stefano De Pietri
(S)
Giorgio Francesco Danelli
(GF)
Laura Albertazzi
(L)
Enrica Bellesia
(E)
Mattia Corradini
(M)
Elena Magnani
(E)
Annalisa Pilia
(A)
Alessandra Polese
(A)
Silvia Storchi Incerti
(SS)
Piera Zaldini
(P)
Bonanno Orsola
(B)
Matteo Revelli
(M)
Carlo Salvarani
(C)
Carmine Pinto
(C)
Francesco Venturelli
(F)
Informations de copyright
© 2021. European Society of Radiology.
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